Echo signal waveform decomposition method and device for Q-switched pulse laser radar, medium and product
By employing a piecewise function and filtering algorithms, the method addresses the inaccuracies of Gaussian-based decomposition for Q-switched pulse laser radar, achieving better waveform decomposition and component extraction.
Patent Information
- Application Number
- CN202411629618.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-14
- Publication Date
- 2025-01-03
AI Technical Summary
Current waveform decomposition methods for Q-switched pulse laser radar assume a Gaussian function, which does not accurately represent the actual pulsed laser waveform, leading to suboptimal decomposition results.
A method using a piecewise function to describe the pulse waveform, combined with filtering and denoising algorithms, to construct a decomposition model that better fits the Q-switched pulse laser radar echo signal, including parameter estimation and waveform fitting algorithms like expectation-maximization or nonlinear least squares.
The method provides improved waveform decomposition results by accurately describing and extracting components of the Q-switched pulse laser radar echo signal, enhancing interpretability and accuracy.
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Figure CN119247396A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of waveform decomposition, and in particular to a method, device, medium and product for decomposing the waveform of an echo signal of a Q-switched pulse laser radar. Background Art
[0002] LiDAR is a common aerosol monitoring solution. It can provide vertical and horizontal distribution information of aerosol particles in the atmosphere, measure the optical properties of aerosols, help study the physical and chemical characteristics of aerosols and their impact on atmospheric radiation transmission and climate change, assess air quality by real-time monitoring of changes in aerosol concentration in the atmosphere, and identify and track pollution sources.
[0003] The laser radar echo pulse can be considered as the superposition of various wave pulses generated by scattering from multiple target surfaces at different distances. Therefore, the single pulses corresponding to different targets can be obtained by decomposing the laser radar echo, and then the corresponding target information can be obtained by analyzing the parameters of these single pulses.
[0004] In recent years, in-depth research has been conducted on the decomposition of lidar waveforms. However, most of the current research on waveform decomposition assumes that the laser pulse is approximately a Gaussian function and chooses the Gaussian function as the decomposition model. However, the actual pulsed laser (especially the Q-switched pulsed laser) is not a perfect Gaussian waveform over time, but has an exponential tail, and cannot obtain a good waveform decomposition result. Summary of the invention
[0005] The purpose of this application is to provide a method, device, medium and product for decomposing the echo signal waveform of a Q-switched pulse laser radar, which can obtain good waveform decomposition results.
[0006] To achieve the above objectives, this application provides the following solutions:
[0007] In a first aspect, the present application provides a method for decomposing an echo signal waveform for a Q-switched pulse laser radar, comprising:
[0008] The original echo signal of the Q-switched pulse laser radar is filtered and denoised using a filtering and denoising algorithm to obtain a pulse waveform.
[0009] Using piecewise function to describe the pulse waveform to obtain a decomposition model;
[0010] Estimate the parameters of the waveform fitting algorithm;
[0011] The decomposition model is used as a fundamental wave and introduced into a waveform fitting algorithm after the estimated parameters to achieve waveform decomposition.
[0012] Optionally, the adopted filtering and denoising algorithm includes at least one or more of sliding filtering, mean filtering, wavelet denoising and empirical mode decomposition denoising.
[0013] Optionally, the decomposition model is expressed as:
[0014]
[0015] Where f(x) represents the piecewise function, A represents the amplitude, x represents the decomposition position, ψ represents the center position of the pulse waveform, and σ 1 represents the scale factor of the exponential curve-like part of the decomposition model, σ 2 It represents the scale factor of the Gaussian curve part in the decomposition model, and the quasi-exponential curve part refers to the part of the decomposition model other than the Gaussian curve part; exp(*) represents an exponential function.
[0016] Optionally, the waveform fitting algorithm is at least an expectation-maximization algorithm or a nonlinear least squares method.
[0017] Optionally, the parameters of the waveform fitting algorithm are estimated, including: estimating the number of waveform components, estimating the initial value of the amplitude of the waveform components, estimating the initial value of the center position of the waveform components, and estimating the initial value of the scale factor.
[0018] Optionally, in the process of estimating the number of waveform components, the extreme points and inflection points of the pulse waveform are determined using the first-order derivative method and the second-order derivative method;
[0019] The number of waveform components is determined based on the number of the extreme value points and the inflection points.
[0020] Optionally, the value at the position of the extreme point is used as the initial value of the center position of the waveform component, or the value at the position corresponding to the inflection point with a larger amplitude among two adjacent inflection points is used as the initial value of the center position of the waveform component.
[0021] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the above-described methods for decomposing echo signal waveforms of a Q-switched pulse lidar.
[0022] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for decomposing the echo signal waveform of a Q-switched pulse lidar.
[0023] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for decomposing the echo signal waveform of a Q-switched pulse lidar.
[0024] According to the specific embodiments provided in this application, this application has the following technical effects:
[0025] The present application provides a method, device, medium and product for decomposing the waveform of the echo signal of a Q-switched pulse laser radar. It inherits the advantage of the classical waveform decomposition method in improving the interpretability of the signal, and at the same time uses a decomposition function that is closer to the actual pulse waveform of the Q-switched pulse laser radar to construct a decomposition model, which can better describe and extract the components of the Q-switched pulse laser radar echo signal and obtain good waveform decomposition results. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0027] Figure 1 A schematic diagram of a flow chart of a method for decomposing an echo signal waveform of a Q-switched pulse laser radar provided in one embodiment of the present application;
[0028] Figure 2 A measured pulse waveform diagram of a Q-switched pulse laser provided in one embodiment of the present application;
[0029] Figure 3 A schematic diagram of a decomposition model provided in an embodiment of the present application;
[0030] Figure 4 This is a result diagram of the decomposition of some measured data using this application;
[0031] Figure 5 This is the result diagram of decomposing some measured data using the classic Gaussian model;
[0032] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0033] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0034] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.
[0035] The method of decomposing the laser radar echo usually includes filtering and fitting the original waveform, wherein the fitting process includes parameter estimation and waveform fitting. Based on this, the present application provides a method for decomposing the echo signal waveform for Q-switched pulse laser radar. Figure 1 As shown, the method includes:
[0036] Step 100: Use a filtering and noise reduction algorithm to filter and reduce noise on the original echo signal of the Q-switched pulse laser radar to obtain a pulse waveform. The original echo signal can be a measured signal obtained by using a horizontal detection Q-switched pulse laser radar.
[0037] Step 101: Use piecewise function to describe the pulse waveform to obtain a decomposition model.
[0038] Step 102: Estimate the parameters of the waveform fitting algorithm.
[0039] Step 103: The decomposition model is used as the fundamental wave and introduced into the waveform fitting algorithm after the estimated parameters to realize waveform decomposition. For example, the waveform fitting algorithm used may be the LM algorithm (Levenberg-Marquardt).
[0040] In the above steps 100 to 103, the classic waveform decomposition method is improved by using a new decomposition model. While retaining the advantages of the original waveform decomposition method, the Q-switched pulse lidar echo signal can be better decomposed.
[0041] In another exemplary embodiment of the present application, the pulse emitted by the laser interacts with the target to form a return signal. Currently, most studies related to waveform decomposition assume that the laser pulse is approximately a Gaussian function and select the Gaussian function as the decomposition model. However, the actual pulse laser (especially the Q-switched pulse laser) is not a perfect Gaussian waveform over time (e.g. Figure 2 (as shown in Figure 2), but there is an exponential tail. Therefore, a piecewise function f(x) is constructed based on the Q-switched laser pulse waveform model to describe the pulse waveform generated by the Q-switched pulse laser radar and serve as a decomposition model. The curve diagram of this decomposition model is shown in Figure 2. Figure 3 shown. Figure 2 The laser pulse waveform shown is obtained by measuring a Q-switched pulse laser using an oscilloscope.
[0042] The decomposition model is expressed as:
[0043]
[0044] Where f(x) represents the piecewise function, A represents the amplitude, x represents the decomposition position, ψ represents the center position of the pulse waveform, and σ 1 represents the scale factor of the exponential curve-like part of the decomposition model, σ 2 It represents the scale factor of the Gaussian curve part in the decomposition model. The quasi-exponential curve part refers to the part of the decomposition model other than the Gaussian curve part. exp(*) represents the exponential function. These parameters control the shape of the decomposition model, and each parameter has a definite meaning for the shape of the decomposition model, which is highly interpretable.
[0045] In another exemplary embodiment of the present application, the filtering and denoising algorithms that can be used include: sliding filtering, mean filtering, wavelet denoising, empirical mode decomposition denoising, etc., but are not limited thereto.
[0046] In another exemplary embodiment of the present application, the parameters of the waveform fitting algorithm are estimated, including: estimating the number of waveform components, estimating the initial amplitude values of the waveform components, estimating the initial center positions of the waveform components, and estimating the initial scale factor values.
[0047] In this embodiment, the method for estimating parameters may refer to a classical waveform decomposition method, or may refer to various optimized waveform decomposition methods according to actual needs.
[0048] In practical applications, the estimation of the parameters of the waveform fitting algorithm can be achieved through the following steps:
[0049] (1) Estimation of the number of waveform components:
[0050] The extreme points and inflection points of the waveform are found by the first-order derivative method and the second-order derivative method. It is believed that one extreme point should correspond to two inflection points. Then the extra inflection points correspond to the extreme point of the overlapping pulse and one of its inflection points (the other inflection point is often difficult to detect because the waveforms overlap each other), then:
[0051] The number of waveform components ≈ the number of extreme points + (the number of inflection points - 2*the number of extreme points) / 2 ≈ the number of inflection points / 2.
[0052] (2) Estimation of the initial value of the center position of the component:
[0053] The position of the extreme point found in step (1) is the initial value of the center position of its corresponding component. The redundant inflection points found in step (1) are grouped into two adjacent groups, and the position corresponding to the point with the larger amplitude in each group can also be considered as the initial value of the center position of the component.
[0054] (3) Estimation of the initial value of the central amplitude of the component:
[0055] The initial value of the central amplitude of each component is the amplitude corresponding to the central position of each component found in step (2).
[0056] (4) Estimation of the scale factor of the component:
[0057] For the component corresponding to the extreme point found in step (2), the initial value of its scale factor is half the distance between the two adjacent inflection points. For the component corresponding to the extra inflection point found in step (2), the initial value of its scale factor is approximately equal to the distance between it and the inflection point with a lower amplitude and the closest position.
[0058] In another exemplary embodiment of the present application, the waveform fitting algorithm used may be an expectation-maximum algorithm (EM), a nonlinear least squares method (such as a Gauss-Newton algorithm, a Levenberg-Marquardt algorithm, etc.), but is not limited thereto.
[0059] In another exemplary embodiment of the present application, in order to more clearly demonstrate the superiority of the present application, a group of classical methods using Gaussian models as decomposition models are added as a control group. Figure 4 and Figure 5 It can be seen that the decomposition model provided by the present application has better results for the case where multiple components overlap, and the restored signal obtained by superimposing each component is closer to the original signal. Figure 4 and Figure 5 In the figure, the dotted curve is the waveform component obtained by the waveform decomposition algorithm, that is, the decomposition result, and its corresponding extreme points are represented by numbers (1), (2), (3) and (4).
[0060] In summary, the waveform decomposition method for the Q-switched pulse lidar echo signal provided in this application has the advantages of sufficient theoretical support, good effect, simple practical operation, and strong universality.
[0061] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store echo signal waveform decomposition data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for decomposing an echo signal waveform for a Q-switched pulse laser radar is implemented.
[0062] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.
[0063] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0064] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0065] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.
[0066] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0067] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.
[0068] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0069] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A method for decomposing the echo signal waveform of a Q-switched pulse laser radar, characterized in that: The echo signal waveform decomposition method for the Q-switched pulse laser radar includes: The original echo signal of the Q-switched pulse laser radar is filtered and denoised using a filtering and denoising algorithm to obtain a pulse waveform. Using piecewise function to describe the pulse waveform to obtain a decomposition model; Estimate parameters of waveform fitting algorithms; The decomposition model is used as a fundamental wave and introduced into a waveform fitting algorithm after the estimated parameters to achieve waveform decomposition.
2. The echo signal waveform decomposition method for Q-switched pulse laser radar according to claim 1, characterized in that: The adopted filtering and denoising algorithms include at least one or more of sliding filtering, mean filtering, wavelet denoising and empirical mode decomposition denoising.
3. The echo signal waveform decomposition method for Q-switched pulse laser radar according to claim 1, characterized in that: The decomposition model is expressed as: Wherein, f(x) represents piecewise function, A represents amplitude, x represents decomposition position, ψ represents center position of pulse waveform, σ1 represents scale factor of quasi-exponential curve part in decomposition model, σ2 represents scale factor of Gaussian curve part in decomposition model, and the quasi-exponential curve part refers to the part of decomposition model except Gaussian curve part; exp(*) represents exponential function.
4. The echo signal waveform decomposition method for Q-switched pulse laser radar according to claim 1, characterized in that: The waveform fitting algorithm is at least an expectation maximization algorithm or a nonlinear least squares method.
5. The echo signal waveform decomposition method for Q-switched pulse laser radar according to claim 1, characterized in that: The parameters of the waveform fitting algorithm are estimated, including: estimation of the number of waveform components, estimation of initial amplitude values of waveform components, estimation of initial center positions of waveform components, and estimation of initial scale factors.
6. The echo signal waveform decomposition method for Q-switched pulse laser radar according to claim 5, characterized in that: In the process of estimating the number of waveform components, the extreme points and inflection points of the pulse waveform are determined; The number of waveform components is determined based on the number of the extreme value points and the inflection points.
7. The echo signal waveform decomposition method for Q-switched pulse laser radar according to claim 6, characterized in that: The value at the position of the extreme point is used as the initial value of the center position of the waveform component, or the value corresponding to the inflection point with a larger amplitude among two adjacent inflection points is used as the initial value of the center position of the waveform component.
8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the echo signal waveform decomposition method for Q-switched pulse laser radar described in any one of claims 1-7.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the echo signal waveform decomposition method for a Q-switched pulse laser radar described in any one of claims 1 to 7 is implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the echo signal waveform decomposition method for a Q-switched pulse laser radar described in any one of claims 1 to 7 is implemented.